@Article{PereiraSRFFCKW:2016:AsFiEm,
author = "Pereira, Gabriel and Siqueira, Ricardo and Ros{\'a}rio, Nilton E.
and Freitas, Karla Maria Longo de and Freitas, Saulo Ribeiro de
and Cardozo, Francielle S. and Kaiser, Johannes W. and Wooster,
Martin J.",
affiliation = "{Universidade Federal de S{\~a}o Jo{\~a}o del Rei (UFSJ)} and
{Instituto Nacional de Pesquisas Espaciais (INPE)} and
{Universidade Federal de S{\~a}o Paulo (UNIFESP)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de
Pesquisas Espaciais (INPE)} and {Universidade Federal de S{\~a}o
Jo{\~a}o del Rei (UFSJ)} and {Max Planck Institute for Chemistry
(MPIC)} and {King’s College London (KCL)}",
title = "Assessment of fire emission inventories during the South American
Biomass Burning Analysis (SAMBBA) experiment",
journal = "Atmospheric Chemistry and Physics",
year = "2016",
volume = "16",
number = "11",
pages = "6961--6975",
abstract = "Fires associated with land use and land cover changes release
large amounts of aerosols and trace gases into the atmosphere.
Although several inventories of biomass burning emissions cover
Brazil, there are still considerable uncertainties and differences
among them. While most fire emission inventories utilize the
parameters of burned area, vegetation fuel load, emission factors,
and other parameters to estimate the biomass burned and its
associated emissions, several more recent inventories apply an
alternative method based on fire radiative power (FRP)
observations to estimate the amount of biomass burned and the
corresponding emissions of trace gases and aerosols. The Brazilian
Biomass Burning Emission Model (3BEM) and the Fire Inventory from
NCAR (FINN) are examples of the first, while the Brazilian Biomass
Burning Emission Model with FRP assimilation (3BEM_FRP) and the
Global Fire Assimilation System (GFAS) are examples of the latter.
These four biomass burning emission inventories were used during
the South American Biomass Burning Analysis (SAMBBA) field
campaign. This paper analyzes and inter-compared them, focusing on
eight regions in Brazil and the time period of 1 September31
October 2012. Aerosol optical thickness (AOT550 nm) derived from
measurements made by the Moderate Resolution Imaging
Spectroradiometer (MODIS) operating on board the Terra and Aqua
satellites is also applied to assess the inventories consistency.
The daily area-averaged pyrogenic carbon monoxide (CO) emission
estimates exhibit significant linear correlations (r, p > 0.05
level, Student t test) between 3BEM and FINN and between 3BEM_ FRP
and GFAS, with values of 0.86 and 0.85, respectively. These
results indicate that emission estimates in this region derived
via similar methods tend to agree with one other. However, they
differ more from the estimates derived via the alternative
approach. The evaluation of MODIS AOT550 nm indicates that model
simulation driven by 3BEM and FINN typically underestimate the
smoke particle loading in the eastern region of Amazon forest,
while 3BEM_FRP estimations to the area tend to overestimate fire
emissions. The daily regional CO emission fluxes from 3BEM and
FINN have linear correlation coefficients of 0.750.92, with
typically 2030 % higher emission fluxes in FINN. The daily
regional CO emission fluxes from 3BEM_FRP and GFAS show linear
correlation coefficients between 0.82 and 0.90, with a
particularly strong correlation near the arc of deforestation in
the Amazon rainforest. In this region, GFAS has a tendency to
present higher CO emissions than 3BEM_FRP, while 3BEM_FRP yields
more emissions in the area of soybean expansion east of the Amazon
forest. Atmospheric aerosol optical thickness is simulated by
using the emission inventories with two operational atmospheric
chemistry transport models: the IFS from Monitoring Atmospheric
Composition and Climate (MACC) and the Coupled Aerosol and Tracer
Transport model to the Brazilian developments on the Regional
Atmospheric Modelling System (CCATT-BRAMS). Evaluation against
MODIS observations shows a good representation of the general
patterns of the AOT550 nm time series. However, the aerosol
emissions from fires with particularly high biomass consumption
still lead to an underestimation of the atmospheric aerosol load
in both models.",
doi = "10.5194/acp-16-6961-2016",
url = "http://dx.doi.org/10.5194/acp-16-6961-2016",
issn = "1680-7324",
label = "lattes: 9873289111461387 5 PereiraSRLFCKW:2016:AsFiEm",
language = "en",
targetfile = "pereira_asessment.pdf",
urlaccessdate = "16 jun. 2024"
}